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This work was developed in the framework of the consultancy Generating a consistent historical times series of activity data from land use change for the development of Costa Rica's REDD+ Reference Level funded by the Forest Carbon Partnership Facility (FCPF). The authors are very grateful to the technicians of the institutions involved in the REDD+ process in Costa Rica, especially FONAFIFO, SINAC, INBio, IMN, PRIAS, MAG and INEC. FONAFIFO was completely involved in the technical process and decision making, supervised the methodological decisions and documents, coordinated the meetings with others institutions and provided and requested all the necessary information. IMN and SINAC participated in different workshops during the methodological design and provided historical maps, land use datasets and imagery essential for several phases of the work. INBio provided a huge volume of ground truth data to the validation process of the maps. Comments and information provided from all these local institutions were essential for the good outcome of the project.

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An Operational Framework for Land Cover Classification in the Context of REDD plus Mechanisms. A Case Study from Costa Rica

Publicat a:Remote Sensing. 8 (7): - 2016-07-01 8(7), DOI: 10.3390/rs8070593

Autors: Fernandez-Landa, Alfredo; Algeet-Abarquero, Nur; Fernandez-Moya, Jesus; Luz Guillen-Climent, Maria; Pedroni, Lucio; Garcia, Felipe; Espejo, Andres; Felipe Villegas, Juan; Marchamalo, Miguel; Bonatti, Javier; Escamochero, Inigo; Rodriguez-Noriega, Pablo; Papageorgiou, Stavros; Fernandes, Erick;

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REDD+ implementation requires robust, consistent, accurate and transparent national land cover historical data and monitoring systems. Satellite imagery is the only data source with enough periodicity to provide consistent land cover information in a cost-effective way. The main aim of this paper is the creation of an operational framework for monitoring land cover dynamics based on Landsat imagery and open-source software. The methodology integrates the entire land cover and land cover change mapping processes to produce a consistent series of Land Cover maps. The consistency of the time series is achieved through the application of a single trained machine learning algorithm to radiometrically normalized imagery using iteratively re-weighted multivariate alteration detection (IR-MAD) across all dates of the historical period. As a result, seven individual Land Cover maps of Costa Rica were produced from 1985/1986 to 2013/2014. Post-classification land cover change detection was performed to evaluate the land cover dynamics in Costa Rica. The validation of the land cover maps showed an overall accuracy of 87% for the 2013/2014 map, 93% for the 2000/2001 map and 89% for the 1985/1986 map. Land cover changes between forest and non-forest classes were validated for the period between 2001 and 2011, obtaining an overall accuracy of 86%. Forest age-classes were generated through a multi-temporal analysis of the maps. By linking deforestation dynamics with forest age, a more accurate discussion of the carbon emissions along the time series can be presented.

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